Principal Investigator

Evangelos K. Oikonomou, MD, DPhil, is a cardiologist and physician-scientist, and an Assistant Professor of Medicine in the Section of Cardiovascular Medicine at Yale School of Medicine. His work focuses on the development and clinical translation of artificial intelligence-enabled digital biomarkers, with an emphasis on computer vision for precision phenotyping in cardiovascular disease. His research aims to create scalable, cost-effective tools that fit into routine clinical workflows to improve diagnosis, risk stratification, and therapeutic decision-making.

Dr. Oikonomou received his MD as valedictorian from the University of Athens and his DPhil in Medical Sciences from the University of Oxford. He subsequently completed the Yale Physician-Scientist Training Program, including residency training in Internal Medicine and clinical and postdoctoral fellowship training in Cardiovascular Medicine. His research has been supported by a Ruth L. Kirschstein National Research Service Award from the National Heart, Lung, and Blood Institute, as well as career development awards from the Robert A. Winn Excellence in Clinical Trials Career Development Award Program, the American Heart Association, the Claude D. Pepper Older Americans Independence Center at Yale School of Medicine, and the Yale Center for Clinical Investigation (KL2).

He has received multiple national and international honors, including Young Investigator Awards from the American Heart Association, the European Society of Cardiology, and the Society of Cardiovascular Computed Tomography; the American Society for Clinical Investigation Emerging Generation Award; and the Wiesman Award from the ATTR Early-Career Research Forum.

Dr. Oikonomou's interdisciplinary research lies at the intersection of cardiovascular and cardiometabolic medicine and focuses on four major areas: (i) the development and clinical translation of adipose tissue imaging biomarkers to elucidate the early links between adiposity and cardiovascular disease; (ii) the design and validation of deep learning algorithms for point-of-care echocardiography to detect both common and under-recognized cardiomyopathies; (iii) the data-driven evaluation of treatment-effect heterogeneity in clinical trials to inform adaptive and precision-enriched trial design; and (iv) the multimodal integration of these approaches into clinical care pathways through clinical informatics. His work has been published in The Lancet, The Lancet Digital Health, JAMA, NEJM AI, JAMA Cardiology, European Heart Journal, JACC, Circulation, and Diabetes Care, among others.

Looking ahead, his research uses multimodal AI to redefine diagnostic and prognostic frameworks across the cardiovascular disease spectrum, from subclinical detection to dynamic risk prediction, with an emphasis on real-world implementation and equitable access to advanced diagnostics.